The Effect of Emotion Regulation-Based Group Therapy on Alexithymia and Experiential Avoidance in Substance Use Patients, with Borderline Personality Disorder
Bibliographic record
Abstract
The purpose of this study was to evaluate the effectiveness of group therapy based on emotion regulation on alexithymia, and experiential avoidance in substance user patient with comorbidity of borderline personality disorder. The research design was quasi-experimental with two experimental and control groups including pre-test and post-test. The statistical population of this study consisted of patients with substance abuse disorder and borderline personality in a psychiatric hospital of Shiraz city; 30 persons (male) were selected using purposive sampling method and randomly assigned in the study: One experimental and one control group (15 males each). The criteria for entry into treatment were borderline personality disorder diagnosis with substance abuse and methadone maintenance treatment. The tools used in this study were Toronto-20 mood disorder questionnaire and Second Edition Acceptance and Practice Questionnaire to assess experiential avoidance. Data were analyzed using covariance analysis and descriptive statistics. The results showed that the scores of alexithymia, and experiential avoidance significantly decreased in the experimental group after the treatment. The results of this study illustrated that group therapy based on emotion regulation can reduce alexithymia, and experiential avoidance in this group of people. Therefore, it is suggested that in addition to pharmacological treatments, psychological treatment methods may also be used to increase the effectiveness of treatment on drug users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".